SummaryFrancisella tularensis is an intracellular bacterial pathogen, and is a category A bioterrorism agent. Within quiescent human macrophages, the F. tularensis pathogenicity island (FPI) is essential for bacterial growth within quiescent macrophages. The F. tularensis-containing phagosome matures to a late endosome-like stage that does not fuse to lysosomes for 1-8 h, followed by gradual bacterial escape into the macrophage cytosol. Here we show that the FPI protein IglD is essential for intracellular replication in primary human monocyte-derived macrophages (hMDMs). While the parental strain replicates robustly in pulmonary, hepatic and splenic tissues of BALB/c mice associated with severe immunopathologies, the isogenic iglD mutant is severely defective. Within hMDMs, the iglD mutant-containing phagosomes mature to either a late endosome-like phagosome, similar to the parental strain, or to a phagolysosome, similar to phagosomes harbouring the iglC mutant control. Despite heterogeneity and alterations in phagosome biogenesis, the iglD mutant bacteria escape into the cytosol faster than the parental strain within hMDMs and pulmonary cells of BALB/c mice. Co-infections of hMDMs with the wild-type strain and the iglD mutant, or super-infection of iglD mutantinfected hMDMs with the wild-type strain show that the mutant strain replicates robustly within the cytosol of hMDMs coinhabited by the wild strain. However, when the wild-type strain-infected hMDMs are super-infected by the iglD mutant, the mutant fails to replicate in the cytosol of communal macrophages. This is the first demonstration of a F. tularensis novel protein essential for proliferation in the macrophage cytosol. Our data indicate that F. tularensis transduces signals to the macrophage cytosol to remodel it into a proliferative niche, and IglD is essential for transduction of these signals.

The aim of this study was to analyze the association between vascular endothelial growth factor (VEGF) expression on tumor cells and other clinicopathologic parameters in breast cancer that could give additional information on its prognostic significance. Immunohistochemical analysis of expression of VEGF, estrogen (ER) and progesterone receptor (PR), HER-2/neu, and Ki67 was performed in 233 breast cancers. VEGF expression estimated semiquantitatively was correlated with all the above-mentioned parameters as well as with clinicopathologic characteristics of breast cancer such as menopausal status of patients, tumor size, histologic and nuclear grade, vascular invasion, and lymph node status. Most of the tumor cells and some stromal components expressed VEGF. A higher percentage of VEGF-positive tumor cells was present in premenopausal patients and in ER-negative tumors. In postmenopausal patients tumors with a higher expression of VEGF were associated not only with ER-negative but also with HER-2/neu-positive tumor cells. These ER-negative tumors were characterized by a higher proliferative activity. Angiogenic switch as well as proliferative activity of breast cancer cells probably are unfavorably dependent on estrogen activity. This negative correlation between VEGF expression and ER status may not only shed more light on tumor biology but may also have future therapeutic implications.

Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.

BackgroundPrognostic and predictive significance of epidermal growth factor receptor (EGFR) in colorectal carcinomas (CRCs) is still controversial. The aim of the present study was to explore and correlate membrane and nuclear EGFR and cyclin-D1 protein expression with EGFR gene status of tumor cells.MethodsImmunohistochemical and FISH analysis was performed on 135 archival formalin fixed and paraffin embedded CRCs.ResultsStrong membrane and strong nuclear EGFR staining was detected in 16% and 57% of cases, respectively, and strong cyclin-D1 expression in 57% samples. Gene EGFR amplification was identified in 5.9% and polysomy in 7.4% of cases, while 87% showed no EGFR gene changes. A statistically significant difference was only found between tumor grade and expression of membrane EGFR, while nuclear EGFR and cyclin-D1 expression was not associated with the clinicopathologic characteristics analyzed. Tumor cells displaying gene amplification and strong protein membrane EGFR expression overlapped, while EGFR gene status showed no correlation with nuclear EGFR and cyclin-D1. There was no association between membrane EGFR and cyclin-D1, whereas nuclear EGFR expression was strongly related to cyclin-D1 expression.ConclusionsStudy results revealed heterogeneity among CRCs, which could have a predictive value by identifying biologically and probably clinically different subsets of tumors with the possibly diverse response to anti-EGFR therapies.

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